EP3351938A1 - Nouveaux biomarqueurs pour évaluer le cancer de l'ovaire - Google Patents

Nouveaux biomarqueurs pour évaluer le cancer de l'ovaire Download PDF

Info

Publication number
EP3351938A1
EP3351938A1 EP17152072.9A EP17152072A EP3351938A1 EP 3351938 A1 EP3351938 A1 EP 3351938A1 EP 17152072 A EP17152072 A EP 17152072A EP 3351938 A1 EP3351938 A1 EP 3351938A1
Authority
EP
European Patent Office
Prior art keywords
ovarian cancer
phosphatidylcholine
residue sum
acyl
ratio
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP17152072.9A
Other languages
German (de)
English (en)
Inventor
Udo Müller
Simon SCHAFFERER
Barbara BURWINKEL
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Biocrates Life Sciences AG
Original Assignee
Biocrates Life Sciences AG
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Biocrates Life Sciences AG filed Critical Biocrates Life Sciences AG
Priority to EP17152072.9A priority Critical patent/EP3351938A1/fr
Priority to CN201880007189.3A priority patent/CN110199198B/zh
Priority to US16/478,819 priority patent/US11506665B2/en
Priority to EP18704150.4A priority patent/EP3571506B1/fr
Priority to PCT/EP2018/051254 priority patent/WO2018134329A1/fr
Priority to JP2019538601A priority patent/JP7169278B2/ja
Publication of EP3351938A1 publication Critical patent/EP3351938A1/fr
Withdrawn legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57407—Specifically defined cancers
    • G01N33/57449—Specifically defined cancers of ovaries
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/92—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving lipids, e.g. cholesterol, lipoproteins, or their receptors
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2405/00—Assays, e.g. immunoassays or enzyme assays, involving lipids
    • G01N2405/04—Phospholipids, i.e. phosphoglycerides
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2405/00—Assays, e.g. immunoassays or enzyme assays, involving lipids
    • G01N2405/08—Sphingolipids
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6803—General methods of protein analysis not limited to specific proteins or families of proteins
    • G01N33/6848—Methods of protein analysis involving mass spectrometry

Definitions

  • the present invention relates to new biomarkers for assessing ovarian cancer being more sensitive, particularly at early stage of disease. Moreover, the present invention relates to a method for assessing ovarian cancer from a patient to be examined, and to a kit for carrying out the method.
  • Metabolomics is a comprehensive quantitative measurement of low molecular weight compounds covering systematically the key metabolites, which represent the whole range of pathways of intermediary metabolism. In a systems biology approach, it provides a functional readout of changes determined by genetic blueprint, regulation, protein abundance and modification, and environmental influence. The capability to analyse large arrays of metabolites extracts biochemical information reflecting true functional end-points of overt biological events while other functional genomics technologies such as transcriptomics and proteomics, though highly valuable, merely indicate the potential cause for phenotypic response. Therefore, they cannot necessarily predict drug effects, toxicological response or disease states at the phenotype level unless functional validation is added.
  • Metabolomics bridges this information gap by depicting in particular such functional information since metabolite differences in biological fluids and tissues provide the closest link to the various phenotypic responses. Needless to say, such changes in the biochemical phenotype are of direct interest to pharmaceutical, biotech and health industries once appropriate technology allows the cost-efficient mining and integration of this information.
  • phenotype is not necessarily predicted by genotype.
  • genotype The gap between genotype and phenotype is spanned by many biochemical reactions, each with individual dependencies to various influences, including drugs, nutrition and environmental factors.
  • metabolites are the quantifiable molecules with the closest link to phenotype.
  • Many phenotypic and genotypic states, such as a toxic response to a drug or disease prevalence are predicted by differences in the concentrations of functionally relevant metabolites within biological fluids and tissue.
  • Ovarian cancer is the second most common and most lethal of all gynaecologic diseases, displaying a world-wide increase of incidence and prevalence over the past decade.
  • women diagnosed between the years 2000 and 2007 showed a mean five-year survival of only around 35%.
  • more than 20,000 new ovarian cancer cases are expected each year leading to over 14,000 deaths (Siegel et al. 2015).
  • the five year survival rate is even less than 25 % for women diagnosed with advanced ovarian cancer stages III or IV (Shapira et al. 2014; Berkenblit und Cannistra 2005; Vaughan et al. 2011). Therefore, a reliable diagnosis with validated methods that could detect ovarian cancer with a high sensitivity in order to correctly assign women with the disease and specificity to avoid false-positive results would be essential.
  • CA-125 Cancer Antigen 125
  • HE4 Human Epididymis Secretory Protein 4
  • FDA U.S. Food and Drug Administration
  • An increase in blood concentrations of CA-125 can be used as an indicator of disease recurrence (Suh et al. 2010).
  • elevated levels of soluble CA-125 has also been found in a variety of other malignancies such as breast cancer, lymphomas, endometriosis or gastric cancer (Norum et al. 2001; Yamamoto et al.
  • CA-125 alone cannot serve as an ideal biomarker for ovarian cancer.
  • CA-125 together with transvaginal sonography is only able to detect around 30% of women with early-stage ovarian cancer (Roupa et al. 2004).
  • the object underlying the present invention is the provision of new biomarkers for assessing ovarian cancer which markers are more sensitive, particularly at early stage of disease.
  • the marker should be easily detectable in a biological sample such as in blood and/or plasma, its level should be consistently related to the degree of ovary injury and its level should change.
  • the inventors based their investigations on metabolomics as it could give insight in the biochemical changes occurring in the ovary during the course of disease and offer several novel and potentially improved biomarkers. Hence, it would be a significant improvement to have metabolic biomarkers for ovarian cancer, which would also give more information about the function of the ovary and the biochemical reactions therein. The inventors found that a more comprehensive picture of all involved pathways and mechanisms is realised when using a panel of metabolites that are altered with progressing ovarian cancer rather than employing only single-markers as provided in the prior art.
  • the present invention provides for new biomarkers (i.e. a new biomarker set) suitable for assessing ovarian cancer which are more sensitive for pathological changes in the ovary, particularly at early stage of disease. Moreover, the present invention also provides for a method for assessing ovarian cancer in a patient, as well as a kit adapted to carry out the method.
  • “Assessing” in the sense of the present invention means the diagnosis of the onset and monitoring of the progression of the disease, in particular the detection and marking of the disease at the different stages.
  • the present invention allows to predict and diagnose ovarian cancer in an improved manner and at an early stage of the disease and provides a more sensitive detection for pathological changes in the ovary.
  • the biomarkers according to the invention are easily detectable in samples, in particular in blood and/or in plasma, their level is consistently related to the degree of ovary disease /injury and their level changes.
  • a biomarker is a valuable tool due to the possibility to distinguish two or more biological states from one another, working as an indicator of a normal biological process, a pathogenic process or as a reaction to a pharmaceutical intervention.
  • a metabolic biomarker provides more comprehensive information than for example a protein or hormone which are biomarkers, but even not metabolic biomarkers.
  • metabolic biomarker as used herein is defined to be a compound suitable as an indicator of the state of ovarian cancer, being a metabolite or metabolic compound occurring during metabolic processes in the mammalian body.
  • the metabolic biomarker (set) measured according to the present invention comprises the following species of metabolites (i.e. analytes) at least
  • the metabolic biomarker set can be extended with at least one metabolite according to Table 1 presenting species of the above-mentioned classes.
  • measuring a set of biomarkers comprising these classes and species of metabolites allows to predict and diagnose ovarian cancer in an improved manner and at an early stage of the disease. In particular, it allows a more sensitive detection for pathological changes in the ovary. If one class or specie of metabolites of this group is omitted or if the number thereof is decreased the assessment of ovarian cancer becomes less sensitive and less reliable. This particularly applies for the early stages of the disease being not reliably-detectable according to known methods using known biomarkers at all. In fact, the measurement of at least
  • the biomarker set further comprises a ratio of selected biomarkers, in particular at least one ratio of at least two biomarkers of i.) to iv.) is determined. By measuring these ratio(s) the diagnostic performance of the biomarker set and the method according to the invention can be further improved.
  • the said ratio has a value of greater than zero.
  • the biomarker set according to the invention further comprises one or more metabolites selected from the group of amino acids, biogenic amines, acylcarnitines, hexoses, sphingolipids and glycerophospholipids. Preferred examples of these classes are presented in Table 1 below. Again, by measuring in addition metabolites of these classes the diagnostic performance of the biomarker set and the method according to the invention can be further improved.
  • a particularly preferred biomarker set is the one wherein the amino acids are selected from arginine, tryptophan, and the ratios are selected from C18:2/lysoPC a C18:2, C18:2/SM (OH) C24:1, Glu/Ala, Glu/PC aa C32:2.
  • body fluid preferably blood
  • body fluid is drawn from the patient to be examined, optionally full blood or serum, or available plasma, and the diagnosis is carried out in vitro/ex vivo, e.g. outside of the human or animal body.
  • the invention therefore also relates to identifying patients having an increased risk and/or an unfavourable prognosis of ovarian cancer, especially in symptomatic and/or asymptomatic patients.
  • the invention also relates to a method for the diagnosis and/or risk classification of patients having ovarian cancer for carrying out clinical decisions, such as the continuative treatment and therapy by means of pharmaceuticals, including the decision of hospitalization of the patient.
  • the assessment is for the diagnosis and/or risk classification, for the prognosis, for differential diagnostic, for early stage detection and recognition.
  • ovarian cancer refers to a type of gynecologic tumors having no or few symptoms in the early stage of a patient or female patient.
  • Ovarian cancer is a malignant disease of the ovary in the genital tract in women, (cf. Pschyrembel, de Gruyter, 263rd edition (2012), Berl in).
  • the term "patient, in particular female patient” is understood to mean any test subject (human or mammal), with the provision that the test subject is tested for ovarian cancer.
  • female patient is understood to mean any female test subject.
  • the invention relates to a kit adapted for carrying out the method, wherein the kit comprises a device which contains one or more wells and one or more inserts impregnated with at least one internal standard.
  • a device which contains one or more wells and one or more inserts impregnated with at least one internal standard.
  • Such a device is in detail described in WO 2007/003344 and WO 2007/003343 .
  • chromatography may comprise GC, LC, HPLC, and UPLC
  • spectroscopy may comprise UV/Vis, IR, and NMR
  • mass spectrometry may comprise ESI-QqQ, ESI-QqTOF, MALDI-QqQ, MALDI-QqTOF, and MALDI-TOF-TOF.
  • FIA-and HPLC-tandem mass spectrometry are generally known to the skilled person.
  • targeted metabolomics is used to quantify the metabolites in the sample including the analyte classes of amino acids, biogenic amines, acylcarnitines, hexoses, sphingolipids and glycerophospholipids.
  • the quantification is carried out using in the presence of isotopically labeled internal standards and determined by the methods as described above.
  • a list of analytes including their abbreviations (BC codes) being suitable as metabolites to be measured according to the invention is indicated in the following Table 1.
  • Table 1 Amino acids and biogenic amines BC code Analyte Ala Alanine Arg Arginine Asn Asparagine Asp Aspartate Cit Citrulline Gln Glutamine Glu Glutamate Gly Glycine His Histidine Ile lsoleucine Leu Leucine Lys Lysine Met Methionine Orn Ornithine Phe Phenylalanine Pro Proline Ser Serine Thr Threonine Trp Tryptophane Tyr Tyrosine Val Valine Ac-Orn Acetylornithine ADMA Asymmetric dimethylarginine SDMA Symmetric dimethylarginine total DMA Total dimethylarginine alpha-AAA alpha-Aminoadipic acid Carnosine Carnosine Creatinine Creatinine Histamine Histamine Kynurenine Kynurenine Met-SO Methioninesulfoxide Nitro-Tyr Nitrotyrosine OH-Pro Hydroxyproline PEA Phenylethylamine
  • the detected signal is a sum of several isobaric lipids with the same molecular weight ( ⁇ 0.5 Da range) within the same class.
  • the signal of PC aa C36:6 can arise from different lipid species that have different fatty acid composition (e.g. PC 16:1/20:5 versus PC 18:4/18:2), various positioning of fatty acids sn-1/sn-2 (e.g. PC 18:4/18:2 versus PC 18:2/18:4) and different double bond positions and stereochemistry in those fatty acid chains (e.g. PC(18:4(6z,9z,12z,15z)/18:2(9z,12z)) versus PC(18:4(9E,11E,13E,15E)/18:2(9z,12z))).
  • the data set for training the classifier consisted of 100 healthy patient control samples, 34 ovarian cancer samples and 80 early stage breast cancer samples, whereby one ovarian cancer sample was removed due to delayed plasma separation, as indicated by pre-analytical quality filtering, leading to 33 ovarian cancer samples.
  • the ovarian cancer samples consisted of 2 FIGO Stage I, 1 FIGO Stage II, 28 FIGO stage III and 2 FIGO stage IV samples (Prat 2014). In the following FIGO stage I and II are designated as early stage ovarian cancer, whereas FIGO stage III and IV are designated as late stage ovarian cancer.
  • the data set for validating the classifier consisted of 50 healthy control samples, 35 ovarian cancer cases and 109 early stage breast cancer patients.
  • the ovarian cancer cases consisted of 5 FIGO stage I, 5 FIGO stage II, 15 FIGO stage III and 6 FIGO stage IV samples. Additionally, 4 samples do not feature any FIGO staging information.
  • a random forest classifier was trained on this resulting set of 9 features, since it showed the best overall performance compared to other classifiers i.e. C5.0, generalized boosted regression modelling, logistic regression, or classification trees as implemented in the R-package caret (Max Kuhn; Jerome H. Friedman 2002; Jerome H. Friedman, Trevor Hastie, Rob Tibshirani 2009; Strobl et al. 2009; Steven L. Salzberg).
  • the features were reduced to a smaller subset of features based on their variable importance measured with the Mean Decrease Gini importance measure.
  • the obtained set of 3 features (C18.2/lysoPC a C18:2, Trp, Ala) consisting of 4 different metabolites was trained with random forest classification by repeated 10 fold cross validation, resulting in an area under the curve (AUC) of 1.0 ( Figure 1 left), a sensitivity of 100%, and a specificity of 100%.
  • the classifier was validated on an independent validation dataset described above.
  • the classifier performance was evaluated by a receiver operator characteristic (ROC) curve, showing an AUC of 0.96 ( Figure 1 right) and by predicting the outcome with the fixed cut-off obtained in the training set leading to 88.5% sensitivity and 100% specificity.
  • ROC receiver operator characteristic
  • 31 of 35 ovarian cancer patients were correctly identified and 50 of 50 healthy controls were correctly identified.
  • this model was used to test the differential diagnosis and included breast cancer patient samples without metastasis for comparison. Thereby 104 of 109 of the breast cancer samples were identified as non-ovarian cancer, corresponding to a specificity of 96.8% and an AUC of 0.94 ( Figure 2 ).
  • the prediction potential of each metabolite from the model as single metabolite and ratio in the training set ( Figure 3A ) and in the validation set ( Figure 3B ).
  • the classifier performance on early stage ovarian cancer patients (10 patients in total) was evaluated by a receiver operator characteristic (ROC) curve, showing an AUC of 0.98 ( Figure 4 left) and by predicting the outcome with the fixed cut-off obtained in the training set leading to 90% sensitivity and 100% specificity.
  • ROC receiver operator characteristic
  • 9 of 10 early stage ovarian cancer patients were correctly identified.
  • Differential diagnosis including breast cancer patients without metastasis for comparison shows an AUC of 0.97 ( Figure 4 right).
  • the prediction potential of each metabolite from the model was investigated as a single metabolite and ratio in the training set ( Figure 5A ) and in the validation set ( Figure 5B ).
  • the classifier performance on late stage ovarian cancer patients was evaluated by a receiver operator characteristic (ROC) curve, showing an AUC of 0.95 ( Figure 6 left) and by predicting the outcome with the fixed cut-off obtained in the training set leading to 88% sensitivity and 100% specificity.
  • ROC receiver operator characteristic
  • 22 of 25 late stage ovarian cancer patients were correctly.
  • Differential diagnosis including breast cancer patients without metastasis for comparison shows an AUC of 0.92 ( Figure 6 right).
  • the prediction potential of each metabolite from the model was investigated as a single metabolite and ratio in the training set ( Figure 7A ) and in the validation set ( Figure 7B ).
EP17152072.9A 2017-01-18 2017-01-18 Nouveaux biomarqueurs pour évaluer le cancer de l'ovaire Withdrawn EP3351938A1 (fr)

Priority Applications (6)

Application Number Priority Date Filing Date Title
EP17152072.9A EP3351938A1 (fr) 2017-01-18 2017-01-18 Nouveaux biomarqueurs pour évaluer le cancer de l'ovaire
CN201880007189.3A CN110199198B (zh) 2017-01-18 2018-01-18 用于评估卵巢癌的代谢生物标志物组
US16/478,819 US11506665B2 (en) 2017-01-18 2018-01-18 Metabolic biomarker set for assessing ovarian cancer
EP18704150.4A EP3571506B1 (fr) 2017-01-18 2018-01-18 Ensemble de biomarqueurs métaboliques pour l'évaluation du cancer de l'ovaire
PCT/EP2018/051254 WO2018134329A1 (fr) 2017-01-18 2018-01-18 Ensemble de biomarqueurs métaboliques pour évaluer le cancer de l'ovaire
JP2019538601A JP7169278B2 (ja) 2017-01-18 2018-01-18 卵巣癌を評価するための新規バイオマーカー

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
EP17152072.9A EP3351938A1 (fr) 2017-01-18 2017-01-18 Nouveaux biomarqueurs pour évaluer le cancer de l'ovaire

Publications (1)

Publication Number Publication Date
EP3351938A1 true EP3351938A1 (fr) 2018-07-25

Family

ID=57860708

Family Applications (2)

Application Number Title Priority Date Filing Date
EP17152072.9A Withdrawn EP3351938A1 (fr) 2017-01-18 2017-01-18 Nouveaux biomarqueurs pour évaluer le cancer de l'ovaire
EP18704150.4A Active EP3571506B1 (fr) 2017-01-18 2018-01-18 Ensemble de biomarqueurs métaboliques pour l'évaluation du cancer de l'ovaire

Family Applications After (1)

Application Number Title Priority Date Filing Date
EP18704150.4A Active EP3571506B1 (fr) 2017-01-18 2018-01-18 Ensemble de biomarqueurs métaboliques pour l'évaluation du cancer de l'ovaire

Country Status (5)

Country Link
US (1) US11506665B2 (fr)
EP (2) EP3351938A1 (fr)
JP (1) JP7169278B2 (fr)
CN (1) CN110199198B (fr)
WO (1) WO2018134329A1 (fr)

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112804936A (zh) * 2018-06-14 2021-05-14 梅塔博洛米克斯股份有限公司 用于预测，诊断和预后包括癌症在内的各种疾病的代谢组学签名
US11409549B2 (en) 2018-10-15 2022-08-09 AIble Inc. Interface for generating models with customizable interface configurations
US10586164B1 (en) 2018-10-15 2020-03-10 AIble Inc. Interface for visualizing and improving model performance
WO2020099683A1 (fr) * 2018-11-16 2020-05-22 Biocrates Life Sciences Ag Métabolites destinés à être utilisés dans la détection et le diagnostic du cancer de l'ovaire
RU2726971C1 (ru) * 2019-12-05 2020-07-17 федеральное государственное автономное образовательное учреждение высшего образования "Национальный исследовательский университет ИТМО" (Университет ИТМО) Способ неинвазивной ранней диагностики эндометриоза
CN117538530A (zh) * 2023-11-07 2024-02-09 中国医学科学院肿瘤医院 一种检测转移性乳腺癌的生物标志组合物和试剂盒及其应用

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2007003343A1 (fr) 2005-06-30 2007-01-11 Biocrates Life Sciences Ag Appareil et procede d'analyse d'un profil de metabolites
CN101832977A (zh) * 2009-03-09 2010-09-15 复旦大学附属妇产科医院 一种卵巢肿瘤血清标志物
US20120129265A1 (en) * 2009-06-02 2012-05-24 Biocrates Life Sciences Ag New biomarkers for assessing kidney diseases
WO2012164525A2 (fr) * 2011-06-01 2012-12-06 Ecole Polytechnique Federale De Lausanne (Epfl) Biomarqueurs de vieillissement
WO2016038157A1 (fr) * 2014-09-10 2016-03-17 Idcgs Clínica De Diagnósticos Médicos Ltda Marqueurs d'évaluation du cancer du sein
WO2016051020A1 (fr) * 2014-10-02 2016-04-07 Zora Biosciences Oy Procédés de détection du cancer de l'ovaire

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1451358A2 (fr) 2001-11-20 2004-09-01 Genentech, Inc. Reseaux et microreseaux cellulaires et tissulaires et methodes d'utilisation
WO2009151967A1 (fr) * 2008-05-28 2009-12-17 Georgia Tech Research Corporation Biomarqueurs métaboliques pour le cancer des ovaires et procédés pour leur utilisation
CN104316701B (zh) * 2008-06-20 2018-05-15 味之素株式会社 女性生殖器癌症的评价方法
US20170097355A1 (en) * 2015-10-06 2017-04-06 University Of Washington Biomarkers and methods to distinguish ovarian cancer from benign tumors
WO2018136085A1 (fr) * 2017-01-20 2018-07-26 Mprobe Inc. Procédés et compositions permettant de fournir une évaluation d'un cancer de l'ovaire à un stade précoce avec des métabolites
CN112804936A (zh) * 2018-06-14 2021-05-14 梅塔博洛米克斯股份有限公司 用于预测，诊断和预后包括癌症在内的各种疾病的代谢组学签名

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2007003343A1 (fr) 2005-06-30 2007-01-11 Biocrates Life Sciences Ag Appareil et procede d'analyse d'un profil de metabolites
WO2007003344A2 (fr) 2005-06-30 2007-01-11 Biocrates Life Sciences Ag Dispositif d'analyse quantitative de profil de metabolite
CN101832977A (zh) * 2009-03-09 2010-09-15 复旦大学附属妇产科医院 一种卵巢肿瘤血清标志物
US20120129265A1 (en) * 2009-06-02 2012-05-24 Biocrates Life Sciences Ag New biomarkers for assessing kidney diseases
WO2012164525A2 (fr) * 2011-06-01 2012-12-06 Ecole Polytechnique Federale De Lausanne (Epfl) Biomarqueurs de vieillissement
WO2016038157A1 (fr) * 2014-09-10 2016-03-17 Idcgs Clínica De Diagnósticos Médicos Ltda Marqueurs d'évaluation du cancer du sein
WO2016051020A1 (fr) * 2014-10-02 2016-04-07 Zora Biosciences Oy Procédés de détection du cancer de l'ovaire

Non-Patent Citations (25)

* Cited by examiner, † Cited by third party
Title
BAIREY, OSNAT; BLICKSTEIN, DORIT; STARK, PINHAS; PROKOCIMER, MIRON; NATIV, HILA MAGEN; KIRGNER, ILIA; SHAKLAI, MATI: "Serum CA 125 as a prognostic factor in non-Hodgkin's lymphoma", LEUKEMIA & LYMPHOMA, vol. 44, no. 10, 2003, pages 1733 - 1738
BERKENBLIT, ANNA; CANNISTRA, STEPHEN A: "Advances in the management of epithelial ovarian cancer", THE JOURNAL OF REPRODUCTIVE MEDICINE, vol. 50, no. 6, 2005, pages 426 - 438
BREIMAN, L., RANDOM FORESTS, MACHINE LEARNING, 2001
DE GRUYTER: "Pschyrembel", 2012
GUAN WEI ET AL: "Ovarian cancer detection from metabolomic liquid chromatography/mass spectrometry data by support vector machines", BMC BIOINFORMATICS, BIOMED CENTRAL, LONDON, GB, vol. 10, no. 1, 22 August 2009 (2009-08-22), pages 259, XP021055704, ISSN: 1471-2105, DOI: 10.1186/1471-2105-10-259 *
HAIYU ZHANG ET AL: "Prediction of advanced ovarian cancer recurrence by plasma metabolic profiling", MOLECULAR BIOSYSTEMS, vol. 11, no. 2, 1 January 2015 (2015-01-01), GB, pages 516 - 521, XP055369586, ISSN: 1742-206X, DOI: 10.1039/C4MB00407H *
JEROME H. FRIEDMAN: "Stochastic gradient boosting", JOURNAL COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2002, pages 367 - 378
JEROME H. FRIEDMAN; TREVOR HASTIE; ROB TIBSHIRANI: "Regularization Paths for Generalized Linear Models via Coordinate Descent", JOURNAL OF STATISTICAL SOFTWARE, 2009
KITAWAKI, JO; ISHIHARA, HIROAKI; KOSHIBA, HISATO; KIYOMIZU, MIYO; TERAMOTO, MARIKO; KITAOKA, YUI; HONJO, HIDEO: "Usefulness and limits of CA-125 in diagnosis of endometriosis without associated ovarian endometriomas", HUMAN REPRODUCTION (OXFORD, ENGLAND), vol. 20, no. 7, 2005, pages 1999 - 2003
LOUPPE; GILLES; WEHENKEL; LOUIS; SUTERA; ANTONIO; GEURTS; PIERRE: "Understanding Variable Importances in Forests of Randomized Trees", PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON NEURAL INFORMATION PROCESSING SYSTEMS, 2013
MAX KUHN: "Building Predictive Models in R Using the caret Package", JOURNAL OF STATISTICAL SOFTWARE, 2008
MOORE, RICHARD G.; MILLER, M. CRAIG; DISILVESTRO, PAUL; LANDRUM, LISA M.; GAJEWSKI, WALTER; BALL, JOHN J.; SKATES, STEVEN J: "Evaluation of the diagnostic accuracy of the risk of ovarian malignancy algorithm in women with a pelvic mass", OBSTETRICS AND GYNECOLOGY, vol. 118, no. 2, 2011, pages 280 - 288, XP009154836
NORUM, L. F.; ERIKSTEIN, B.; NUSTAD, K: "Elevated CA125 in breast cancer-A sign of advanced disease", TUMOUR BIOLOGY: THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR ONCODEVELOPMENTAL BIOLOGY AND MEDICINE, vol. 22, no. 4, 2001, pages 223 - 228
PRAT; JAIME: "Staging classification for cancer of the ovary, fallopian tube, and peritoneum", INTERNATIONAL JOURNAL OF GYNAECOLOGY AND OBSTETRICS: THE OFFICIAL ORGAN OF THE INTERNATIONAL FEDERATION OF GYNAECOLOGY AND OBSTETRICS, vol. 124, no. 1, 2014, pages 1 - 5
ROUPA, ZOI; FAROS, EFTHIMIOS; RAFTOPOULOS, VASILIOS; TZAVELAS, GEORGIOS; KOTROTSIOU, EVANGELIA; SOTIROPOULOU, PENELOPE ET AL.: "Serum CA 125 combined with transvaginal ultrasonography for ovarian cancer screening", IN VIVO (ATHENS, GREECE), vol. 18, no. 6, 2004, pages 831 - 836
SHAPIRA, I; OSWALD, M.; LOVECCHIO, J.; KHALILI, H.; MENZIN, A.; WHYTE, J. ET AL.: "Circulating biomarkers for detection of ovarian cancer and predicting cancer outcomes", BRITISH JOURNAL OF CANCER, vol. 110, no. 4, 2014, pages 976 - 983
SIEGEL, REBECCA L.; MILLER, KIMBERLY D.; JEMAL, AHMEDIN: "Cancer statistics", CA: A CANCER JOURNAL FOR CLINICIANS, vol. 65, no. 1, 2015, pages 5 - 29
STEVEN L. SALZBERG: "C4.5: Programs for Machine Learning", MACHINE LEARNING, 1994
STROBL, CAROLIN; MALLEY, JAMES; TUTZ, GERHARD: "An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests", PSYCHOLOGICAL METHODS, vol. 14, no. 4, 2009, pages 323 - 348, XP055246620, DOI: doi:10.1037/a0016973
SUH, K. STEPHEN; PARK, SANG W.; CASTRO, ANGELICA; PATEL, HIREN; BLAKE, PATRICK; LIANG, MICHAEL; GOY, ANDRE: "Ovarian cancer biomarkers for molecular biosensors and translational medicine", EXPERT REVIEW OF MOLECULAR DIAGNOSTICS, vol. 10, no. 8, 2010, pages 1069 - 1083, XP055176684, DOI: doi:10.1586/erm.10.87
TIBSHIRANI, ROBERT; BIEN, JACOB; FRIEDMAN, JEROME; HASTIE, TREVOR; SIMON, NOAH; TAYLOR, JONATHAN; TIBSHIRANI, RYAN J.: "Strong rules for discarding predictors in lasso-type problems", JOURNAL OF THE ROYAL STATISTICAL SOCIETY. SERIES B, STATISTICAL METHODOLOGY, vol. 74, no. 2, 2012, pages 245 - 266
VAUGHAN, SEBASTIAN; COWARD, JERMAINE I.; BAST, ROBERT C.; BERCHUCK, ANDY; BEREK, JONATHAN S.; BRENTON, JAMES D. ET AL.: "Rethinking ovarian cancer: recommendations for improving outcomes", NATURE REVIEWS. CANCER, vol. 11, no. 10, 2011, pages 719 - 725
WEI, S. U.; LI, HUI; ZHANG, BEI: "The diagnostic value of serum HE4 and CA-125 and ROMA index in ovarian cancer", BIOMEDICAL REPORTS, vol. 5, no. 1, 2016, pages 41 - 44
YAMAMOTO, MANABU; BABA, HIDEO; TOH, YASUSHI; OKAMURA, TAKESHI; MAEHARA, YOSHIHIKO: "Peritoneal lavage CEA/CA125 is a prognostic factor for gastric cancer patients", JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY, vol. 133, no. 7, 2007, pages 471 - 476, XP019517731, DOI: doi:10.1007/s00432-006-0189-2
ZHANG YANGYANG ET AL: "High resolution mass spectrometry coupled with multivariate data analysis revealing plasma lipidomic alteration in ovarian cancer in Asian women", TALANTA, ELSEVIER, AMSTERDAM, NL, vol. 150, 11 December 2015 (2015-12-11), pages 88 - 96, XP029402347, ISSN: 0039-9140, DOI: 10.1016/J.TALANTA.2015.12.021 *

Also Published As

Publication number Publication date
EP3571506B1 (fr) 2021-09-01
JP7169278B2 (ja) 2022-11-10
JP2020506382A (ja) 2020-02-27
CN110199198B (zh) 2022-09-06
WO2018134329A1 (fr) 2018-07-26
US20200386762A1 (en) 2020-12-10
EP3571506A1 (fr) 2019-11-27
CN110199198A (zh) 2019-09-03
US11506665B2 (en) 2022-11-22

Similar Documents

Publication Publication Date Title
US11506665B2 (en) Metabolic biomarker set for assessing ovarian cancer
US11493516B2 (en) Biomarkers for assessing breast cancer
EP3775906B1 (fr) Détection et diagnostic du cancer du sein à base de métabolite
JP7288283B2 (ja) 小児がん検査用尿中代謝物マーカー
JP2023110034A (ja) 臨床的介入のための妊娠の進展および早期流産の評価方法およびその応用
WO2018184112A1 (fr) Procédés de test d'urine basé sur une spectrométrie de masse à haut débit pour des affections colorectales
JPWO2018066621A1 (ja) 大腸癌の評価方法、評価装置、評価プログラム、評価システム、及び端末装置
JP7337018B2 (ja) 取得方法、算出方法、評価装置、算出装置、評価プログラム、算出プログラム、評価システム
EP2927691B1 (fr) Biomarqueurs pour évaluer le VIH
CN109983137A (zh) 用于预测暴露于孕激素的怀孕女性的早产的生物标志物
ES2688121T3 (es) Medios y procedimientos de diagnóstico de la reaparición de cáncer de próstata después de prostatectomía
JP2023101023A (ja) 取得方法、算出方法、評価装置、算出装置、評価プログラム、算出プログラム、及び評価システム
EP3859339A1 (fr) Méthode d'évaluation de déficience cognitive légère, méthode de calcul, dispositif d'évaluation, dispositif de calcul, programme d'évaluation, programme de calcul, support d'enregistrement, système d'évaluation et dispositif terminal
CN114166977A (zh) 预测妊娠个体血糖值的系统
ES2728669T3 (es) Procedimientos para determinar el riesgo de diabetes tipo 1 mediante biomarcadores de proteínas séricas
JP7435856B2 (ja) 取得方法、算出方法、評価装置、算出装置、評価プログラム、算出プログラム、記録媒体、及び評価システム
WO2023170045A1 (fr) Procédé de diagnostic de l'endométriose chez un sujet
WO2020099683A1 (fr) Métabolites destinés à être utilisés dans la détection et le diagnostic du cancer de l'ovaire
US20160245814A1 (en) Biomarkers for kidney cancer and methods using the same
BRPI1105603B1 (pt) Método de identificação de perfis de metabólitos para determinação da resistência de células cancerosas de leucemia linfoide aguda (lla) à quimioterapia

Legal Events

Date Code Title Description
PUAI Public reference made under article 153(3) epc to a published international application that has entered the european phase

Free format text: ORIGINAL CODE: 0009012

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: THE APPLICATION HAS BEEN PUBLISHED

AK Designated contracting states

Kind code of ref document: A1

Designated state(s): AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR

AX Request for extension of the european patent

Extension state: BA ME

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: THE APPLICATION IS DEEMED TO BE WITHDRAWN

18D Application deemed to be withdrawn

Effective date: 20190126